Non-linear optical microscopy is a powerful, non-invasive imaging technique with intrinsic three-dimensional (3D) sectioning capabilities. Here, we utilize two non-linear optical processes, two-photon excited fluorescence and second harmonic generation, to assess the organization of a decellularized rat heart. No stains or fluorescent dyes were used to acquire these image volumes. Rather, we used a titanium:sapphire laser tuned to a 800nm wavelength to excite endogenous fluorophores in the extracellular matrix that emit at 525nm (shown in green). The non-centrosymmetric organization of collagen molecules also produces a unique interaction with the 800nm light, causing a frequency doubling effect which enables collagen visualization at the 400nm wavelength. By acquiring image volumes with 10x and 63x objectives, the tissue can be visualized from the scale of an entire cross-section of the heart, down to the scale of individual collagen fibers. Decellularized tissue can be used for a variety of regenerative medicine applications due to the lack of any cellular antigens that would otherwise produce an immune response and potential rejection of the tissue. The cells are removed from the tissue using specialized detergents, and non-linear optical microscopy can be used to assess the 3D organization of the remaining extracellular matrix proteins. Because this imaging procedure is non-destructive and uses endogenous sources of contrast, these decellularized tissues can be used for tissue engineering applications after imaging. The upper-left image volume size is 1.50 x 1.50 x 0.37 mm, and the image on the right is 238 x 238 x 100 µm. colored using Matlab, reconstructed and rendered using Osirix, and the multi-scale illustration was assembled in Adobe Photoshop.

Second Place: A New Bioinformatics of Shape for Planarian Regeneration Modeling

Planarians are flat worms with a complex body, including a complete nervous system with brain and eyes, and an outstanding regenerative capacity: cutting a worm in half produces two complete worms, where the head fragment regenerates a new tail and the tail fragment regenerates a new head. Understanding the robust self-assembly and regenerative abilities found in organisms such as planaria would have tremendous implications for basic developmental biology, regenerative biomedicine, and the engineering of resilient cybernetic systems. Current research is producing an ever-increasing amount of information about biological pattern formation under sophisticated molecular perturbations. However, the genetic networks being uncovered do not specify anatomy or the dynamic regulation of shape; after more than 100 years of research, no single model has yet been proposed that can explain more than one or two key features of planarian regeneration. Overcoming the gulf between molecular-genetic data and a true understanding and control of 3-dimensional shape requires the development of new computational tools – a bioinformatics of shape.
Using the planarian data as a test bed, we designed a new formalism to unambiguously encode the diverse set of regenerative experiments and the morphologies resulting from these manipulations. We used this formalism to standardize the natural-language results found in the planarian literature, and created a centralized database containing more than 300 experiments and a user-friendly software tool that permits any scientist to visualize and mine the dataset of experiments and results. Furthermore, the formal database of experiments permits the application of artificial intelligence (AI) tools to help scientists discover constructive, algorithmic models of regenerative patterning.
The illustration shows the diversity of experiments in the database, and an overview of the proposed new Bioinformatics of Shape. This level of abstraction facilitates artificial reasoning algorithms to identify models of regeneration control pathways that match real results in the database. Such a formal database will be essential in the discovery of models of complex biological pattern formation in many subfields, allowing the extraction of knowledge from data, and leading to a profound understanding of adaptive self-regulation that will impact regenerative medicine, synthetic biology, and the information sciences.

This is a thermal FEM (finite element method) model of a laboratory reactor we are developing that is used to rapidly melt slag. This slag is representative of a byproduct of the iron refining process; it contains a small percentage of iron oxide. After the slag has been melted, we plan to electrochemically separate the iron oxide in the slag into iron and oxygen. One of the long term benefits of studying this molten oxide electrolysis (MOE) process is that iron could be refined and steel could be produced in a process that does not produce carbon dioxide. Currently, the industrial processes to refine iron and produce steel are the third largest carbon dioxide producing sector in the world. A well developed MOE process could help to reduce greenhouse gas production. The development of a thermal reactor, which this model is helping us to do, allows molten oxide electrolysis experiments to be run very quickly (in minutes). Our collaborators at MIT are able to run similar experiments with larger devices, but they also require multiple days to perform a single experiment since heating and cooling is slow.
The colors shown are representative of different temperatures, with red being the hottest and blue the coolest. This model shows that when the heating element (red) is at its maximum allowable temperature, the slag (above the heating element, colored orange) will not melt completely. This is a problem that we experienced in the lab, and this model verifies that this reactor design will not perform properly. Thus, we have started testing a new reactor design and are working on modeling that to confirm its performance.
This model is unique because: 1) to our knowledge, an induction based molten oxide reactor has not been developed (or modeled), 2) it incorporates three modes of heat transfer (conduction, convection, and radiation), and 3) all material properties are temperature dependent. Developing this model is challenging since ANSYS is code-based (not user friendly), there is no precedent for this model, and determining temperature dependent material properties requires an extensive literature search and significant mathematical analysis before FEM.